XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: SYNTHETIC APERTURE RADAR (SAR) AND OPTICAL IMAGERY DATA FUSION: CROP YIELD ANALYSIS IN SOUTHEAST ASIA S. M. Parks

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia SYNTHETIC APERTURE RADAR (SAR) AND OPTICAL IMAGERY DATA FUSION: CROP YIELD ANALYSIS IN SOUTHEAST ASIA S. M. Parks“ * Exelis Visual Information Solutions, 4990 Pearl East Circle, Boulder, Colorado, 80304 Working Group VII/6 KEY WORDS: Remote Sensing, SAR, Radar, Data Fusion, LandSAT ETM+, ENVISAT ASAR, ENVI, SARscape ABSTRACT: With the expanding energy crisis and rising food prices, crop yield analysis in Southeast Asia is an increasingly important topic in this region. Rice is the most important food crop in Southeast Asia and the ability to accurately predict crop yields during a growing season is useful for decision-makers, aid providers, and commercial trade organizations. The use of optical satellite image data by itself is difficult due to the almost constant cloud in many parts of Southeast Asia. However, Synthetic Aperture Radar (SAR), or SAR data, which can image the Earth's surface through cloud cover, is suitable for many agricultural purposes, such as the detection of rice fields, and the identification of different crop species. Crop yield analysis is difficult in this region due to many factors. Rice cropping systems are often characterized by the type of rice planted, the size of rice field, the sowing dates for different fields, different types of rice cropping systems from one area to another, as well as cultural practices such as sowing and transplanting. This paper will discuss the use of SAR data fused with optical imagery to improve the ability to perform crop yield analysis on rice crops in Southeast Asia. 1. INTRODUCTION Food source security is a major concern, particularly in Asia, due to the rapid population expansion happening in that region. Accurate evaluations of food crops can be difficult in many countries due to the lack of information available regarding yields. The regular usage of optical satellite image data for crop yield analysis is difficult due to the almost constant cloud in many parts of Southeast Asia. However, SAR data, which can image the Earth's surface through cloud cover, is suitable for many agricultural purposes, such as the detection of rice fields, and the identification of different crop species. Crop yield analysis is difficult in this region due to many factors. Rice cropping systems are often characterized by the type of rice planted, the size of rice field, the sowing dates for different fields, different types of rice cropping systems from one area to another, as well as cultural practices such as sowing and transplanting. Space-borne radar imagery has great potential for the delineation and monitoring of rice crop paddies. SAR images have proven to be suitable for many agricultural remote sensing purposes, for example, detection of agricultural land such as rice fields, and even identification of different crop species is possible using well-timed SAR images (Karjalainen, Kuittinen, Junnikkala, Karvonen, Nguyen, & Tran, 2010). Due to the abundance of cloud coverage in Southeast Asia, SAR data is often a better choice over optical data (Abu Bakar, Shaari, Chuah, & Ewe, 1997). Multi-temporal and multi-sensor data fusion has also been successfully used to identify irrigated rice fields. Rice is often planted in paddy fields and grows in distinct stages, including germination, emergence, tillering, heading, and maturing (Wang, 2009). Rice backscatter coefficients in SAR imagery display higher temporal variation than other types of land cover (Wang, 2009). Rice in the planting stage exhibits a lower backscatter coefficient from flooded water because the rice plant is short and sparse, whereas, in the tillering stage, the backscatter coefficient increases rapidly when more tillers emerge and develop into a denser canopy (Wang, 2009). The backscatter coefficient is slightly less in the late heading stage when the leaves start to dry up and mature (Wang, 2009). It is possible to monitor the rice growth stage during crop yield analysis, by measuring the backscattering coefficient from the plants as a function of time if radar images are acquired at appropriate time intervals during the growing season. Due to the nature of rice growth, there is a rapid increase in biomass during the 30-day vegetative phase for the short growth duration rice, so it is necessary to acquire time series data on a monthly basis in order to capture the changes in backscatter between the beginning of the planting cycle and the end of the reproductive stage (Wang, 2009). Generally, the beginning of a rice season would be identified by a low backscatter in the time series when the field was inundated while the end of the reproductive stage is characterized by a high backscatter (Wang, 2009). In this study, multi-temporal ENVISAT ASAR APS dual polarization (HH, HV) data from five different dates in 2011 were used to estimate rice crop yields in the Ganges Delta of Bangladesh. This data corresponds to the growing season of Aman rice crops, which typically occurs from July through November. 2. METHODS Ten ENVISAT ASAR APS datasets (5 HH and 5 HV datasets) in the original ESA format that were acquired over the same track and frame (i.e. same viewing geometry) were used in this example. The ENVISAT ASAR data were

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